Diagnosis of vascular diseases using three-dimensional imaging requires visualization of the blood flow through the corresponding vessels. Treatment generally takes place with minimal invasion using catheters, which are inserted into the corresponding blood vessel. In order to be able to plan a minimally invasive intervention as precisely as possible and in particular to be able to carry it out exactly, the physician requires information about the position and spread of the vessels (location information) as well as the most accurate information possible about the blood flow through the corresponding vessel (temporal information). While aneurysms generally show up very clearly in the corresponding images, stenoses are generally relatively difficult to see. Instead the angiograms show points in the vessels where the through-flow of blood is much reduced. If stenosis leads to the complete occlusion of a vessel, this means that the corresponding vessel and all the vessels supplied by it are no longer identifiable in the x-ray recording. The three-dimensional visualization of the blood flow therefore provides the physician with important information about the degree of constriction or widening of a vessel and any possible effect on other vessels.
In a clinical situation the diagnosis of vascular diseases is currently based on temporal two-dimensional angiography sequences (showing the blood flow) or static three-dimensional data sets, which generally show a completely filled vessel tree.
The two-dimensional angiography sequences are generated from a view with constant C-arm alignment as a contrast agent is briefly injected. The angiography sequences show the temporal propagation of the contrast agent through the required vessels. Generally a reference image without contrast agent is acquired at the start of the sequence and this is subtracted from all subsequent recordings in the sequence, in order to see only the part of the vessel tree filled with contrast agent in the images. The method is also known as digital subtraction angiography (DSA). However the two-dimensional angiography sequences only supply information with local two-dimensional resolution, not information with spatial (=local three-dimensional) resolution.
DE 10 2004 018 499 A1 discloses a determination method of the type described above. With this method the computer determines a corresponding presence distribution with local three-dimensional resolution in temporally ascending order at least for some of the acquisition times. The presence distribution is related to the volume data set. With the exception of the temporally first acquisition time
the computer uses the group of x-ray images assigned to the respective acquisition time and the volume data set to determine a first preliminary presence distribution,
the computer uses the presence distribution determined for the preceding acquisition time and a vascular structure of the vascular system to determine a second preliminary presence distribution and
assigns the presence of the substance respectively to locations in the volume data set, which are components of the vascular system (vascular locations), if both the first and second preliminary presence distributions indicate the presence of the substance for the respective vascular location and otherwise assigns non-presence of the substance.
In other words: with the procedure according to the prior art vascular locations are excluded, if they are not classified as “substance present” according to both presence distributions.
The procedure according to the prior art already has significant advantages compared with the locally purely two-dimensional processing of the angiography sequence. For it is possible in some instances to map the blood flow from the two-dimensional to the three-dimensional. The procedure according to the prior art also has disadvantages however. In particular the “hard” exclusion of vascular locations means that errors in individual x-ray images of the angiography sequence can no longer be corrected during automated evaluation of the angiography sequence, even if they can be identified as errors based on further x-ray images.
A similar disclosure can be taken from the technical article “Integrating X-ray angiography and MRI for endovascular interventions” by T. P. L. Roberts et al., Netherlands, Philips Medical Systems, November 2000, MEDICA MUNDI, vol. 44/3, pages 2 to 9, ISSN 0025-7664.
From the technical article “Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering” by H. Shim et al., Elsevier 2006, Computer Methods and Programs in Biomedicine, vol. 84 (2006), pages 135 to 145 it is known that probability distributions based on particle filters can be used when segmenting vessels.
From the technical article “Registration of 3D Angiographic and X-Ray Images Using Sequential Monte Carlo Sampling” by C. Florin et al., Springer-Verlag 2005, Computer Vision for Biomedical Image Applications, Lecture Notes in Computer Science, vol. 3765, pages 427 to 436, ISSN 0302-9743, it is known that probability distributions based on particle filters can be used during 2D to 3D registration.